NCT07305636

AI Models vs Non-Invasive Fibrosis Scores in MAFLD Diagnosis

Assessing the Utility of AI Models in MAFLD Diagnosis: Comparison With Traditional Non-Invasive Fibrosis Scores.

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 522 (actual)
• Sponsor: Tanta University · Academic / Other
• Sex: All
• Age: 18 Days
• Healthy volunteers: —

Summary

This study evaluates the accuracy of artificial intelligence (AI) models using FibroScan and clinical data to predict hepatic fibrosis in Egyptian patients with metabolic-associated fatty liver disease (MAFLD). The performance of the AI models will be compared with conventional noninvasive fibrosis scores (FIB-4, APRI, NAFLD fibrosis score, and FAST). The goal is to improve early, noninvasive diagnosis of fibrosis and reduce reliance on liver biopsy.

Conditions

• MAFLD
• AI (Artificial Intelligence)

Timeline

• Start date: 2025-05-13
• Primary completion: 2025-08-30
• Completion: 2025-11-30
• First posted: 2025-12-26
• Last updated: 2025-12-26

Locations

1 site across 1 country: Egypt